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For marketing-ops + RevOps + SRE leadership

Every team should see the alerts they need — not the firehose

Per-team pub-sub fan-out on top of multi-location anomaly detection. Six-axis pipeline (Detect, Forecast, Correlate, Route, Suppress, Subscribe) with delivery adapters for Slack, email, PagerDuty, and webhook.

By Jay Christopher11 min read

What this gets you

  • Per-team pub-sub fan-out — marketing- ops, CS, SRE, per-location managers, executive audiences each receive only the alerts their team acts on.
  • Six-axis anomaly pipeline — Detect, Forecast, Correlate, Route, Suppress, Subscribe. The five upstream stages produce signal; Subscribe delivers it.
  • Per-vertical subscription overrides — QSR teams subscribe differently than cannabis teams subscribe differently than healthcare teams. Per- vertical routing rules respected.
  • Delivery-adapter library — Slack channels per team and per location, email per role, PagerDuty for on-call rotations, webhook for downstream automation, Teams + SMS where appropriate.
  • Audit trail + DLQ + ack/retry + observability — every alert routed gets a versioned record; failed deliveries land in a dead-letter queue; per-consumer lag and DLQ depth visible on the operator dashboard.

Marketing-ops Slack at 8am: 47 alerts. CS dashboard: 23. SRE pager: 31.

Multi-location operators running modern anomaly detection generate dozens-to-hundreds of alerts per day. Per-location two-sigma outliers, cross-stream correlations on root-cause incidents, predictive forecasting on lagging indicators, severity-routed pages, false-positive-suppressed survivors. The detection layer produces signal. Without subscription fan-out, every team sees every alert. Marketing-ops drowns in infra noise; SRE drowns in data-quality noise; CS develops alert fatigue toward the lost-call alerts that actually pay back the system.

Multi-stream subscription is the final stage of the six-axis anomaly pipeline. The first five stages (Detect, Forecast, Correlate, Route, Suppress) compose as a sequential chain that produces curated alerts at the end of the chain. Subscribe then fans those alerts out via pub-sub to per-team consumer groups. Marketing- ops subscribes to data-quality and conversion-anomaly topics; CS subscribes to lost-call and customer- impact topics; SRE subscribes to infra and pipeline- health topics; per-location managers subscribe to their own location’s outlier topics; executives subscribe to severity-tier-one rollups only.

Per-vertical routing layers on top — a QSR system subscribes differently than a cannabis system subscribes differently than a healthcare system. The vertical-specific rules respect regulatory overlay (cannabis has per-state alert rules) and category operating norms (healthcare needs HIPAA-aware alert routing). Per-location overrides allow individual franchisees to tune their own subscription preferences inside the corporate-governed default policy.

For a multi-location operator with five consumer teams and 200 locations, the subscription layer turns a shared firehose into per-team curated streams. The 47-alert morning becomes a 9-alert morning for marketing-ops, an 11-alert morning for CS, and a 4-alert morning for SRE — each one actionable.

What is in market — and what each category leaves to you

The event-broker layer is mature. The per-team subscription logic, per-vertical routing, and delivery-adapter library are operator-side wiring.

Enterprise event brokers — Apache Kafka, Confluent, Apache Pulsar, RabbitMQ, NATS, Redpanda

Excellent transport infrastructure with deep partitioning, consumer-group coordination, schema registry, and delivery-semantics control. The per-team routing logic and the marketing-ops-specific topic schema are operator-built on top.

Cloud-native event brokers — AWS EventBridge, Google Pub/Sub, Azure Service Bus

Strong managed-service options with rule-based filtering at the broker layer. The per-vertical override policy, the multi-adapter delivery library, and the ROI-measurement layer are operator- side.

SRE alert pub-sub — PagerDuty, Opsgenie, Splunk Alert Manager, BigPanda

Excellent for SRE on-call rotations and incident routing. Adjacent surface to marketing-ops alert routing — they handle the SRE consumer side well; the marketing-ops pub-sub fan-out and the per- location subscription model are operator-side.

Marketing-ops event streams — Segment Functions, mParticle Audiences, RudderStack pub-sub, Census Reverse-ETL

Strong for customer-data event routing and reverse-ETL to downstream marketing tools. The anomaly-detection-driven alert subscription layer for per-team and per-location consumers is operator- side.

Single shared Slack channel for everything

The status quo at most multi-location operators scaling beyond two teams. One channel receives every alert from every system; teams mute notifications; important alerts go unread; the operator finds out about per-location outliers when the franchisee emails.

The pipeline, end to end

  1. Six-axis anomaly pipeline upstream. Detect (nine-stream coverage), Forecast (predictive anomalies), Correlate (cross-stream root cause), Route (severity-tiered routing), Suppress (false-positive noise reduction), then the Subscribe stage covered here.
  2. Pub-sub topic design. Topics per concern (data-quality, lost-call, infra, conversion-anomaly, location-outlier, vertical- compliance) with sub-topics per location, per vertical, and per severity tier. Topic naming follows a consistent convention so consumers subscribe deterministically.
  3. Per-team subscription rules. Marketing-ops subscribes to data-quality and conversion-anomaly. CS subscribes to lost-call and customer-impact. SRE subscribes to infra and pipeline- health. Per-location managers subscribe to their own location-outlier topics. Executives subscribe to severity-tier-one rollups.
  4. Per-vertical routing overrides. Cannabis operators get per-state alert filtering; healthcare gets HIPAA-aware routing; financial- services gets FINRA-aware constraints. The override policy layers on top of the per-team default.
  5. Per-location override workflow. Franchisees tune their own subscription preferences inside the corporate default. Override approvals tracked; corporate retains visibility on per-location subscription drift.
  6. Delivery-adapter library. Slack (channels per team and per location), email (role- based digests), PagerDuty (on-call rotations), webhook (downstream automation), Microsoft Teams (Microsoft- stack operators), SMS (high-severity per-location notifications). Each adapter respects per-team rate limits, quiet hours, and channel preferences.
  7. Throttling and back-pressure. Per- consumer rate limits prevent any single subscriber from being flooded; back-pressure signals to upstream producers when a consumer is overwhelmed so the producer slows rather than dropping.
  8. Dead-letter queue. Failed deliveries land in a DLQ with retry policies. Persistent failures surface to the operator dashboard with the failing adapter and the message envelope attached.
  9. Ack and retry semantics. Idempotent delivery with at-least-once semantics by default; exactly-once where the use case demands it (per- location SMS, regulatory-impact alerts).
  10. Ordering guarantees. Partition key per location plus per stream produces ordered processing within partition. Cross-partition ordering is not guaranteed; topic design respects this.
  11. Schema evolution. Schema registry with backward-compatibility checks. Consumers pin schema versions; producers evolve schemas additively; breaking changes route through a versioned migration workflow.
  12. Per-team observability. Consumer lag, DLQ depth, delivery success rate, alert-to-action latency per team. Operator dashboard surfaces drift and overload before it becomes a missed alert.

Frequently asked

What is an event broker?

An event broker is the infrastructure that accepts published events from producers and routes them to subscribers based on topic, filter rules, and delivery semantics. Apache Kafka, Confluent, RabbitMQ, NATS, Redpanda, AWS EventBridge, Google Pub/Sub, and Azure Service Bus are the canonical implementations. They handle the transport — what they do not natively handle is the per-team routing logic, per-vertical override policy, and delivery-adapter layer that turns generic pub-sub into a marketing-ops alert subscription system.

Why do teams drown in alerts when the architecture is unified?

When every alert stream broadcasts to every consumer, the result is the firehose problem. Marketing-ops at 8am sees the SRE infra alerts they cannot act on; SRE sees marketing-ops data-quality alerts they cannot interpret; CS sees both and develops alert fatigue toward the lost-call alerts that actually matter. The firehose architecture trains every team to mute notifications, which then misses the alerts that need attention.

How is this different from Kafka, Confluent, RabbitMQ, AWS EventBridge, Google Pub/Sub, or Azure Service Bus?

Those platforms are excellent event-broker infrastructure — they handle the transport, topic partitioning, consumer-group coordination, and delivery semantics at production scale. The per-team subscription layer (which team subscribes to which topics with which filter rules), the per-vertical routing overrides, the delivery-adapter library (Slack + email + PagerDuty + webhook), the audit trail, and the ROI measurement are operator-side wiring above whichever event broker you license.

What is sequential-then-pub-sub-fan-out topology?

A six-axis anomaly pipeline runs as a sequential chain (Detect → Forecast → Correlate → Route → Suppress) producing curated alerts at the end of the chain. The Subscribe stage then fans those curated alerts out via pub-sub to per-team consumer groups. Sequential processing produces the quality of the alerts; pub-sub fan-out gets the right alerts to the right team. The two patterns combine cleanly — the sequential chain is the producer, the pub-sub fan-out is the routing layer downstream.

How does this connect to two-sigma outlier flagging and the broader anomaly pipeline?

Two-sigma outlier flagging produces per-location anomaly signals. Cross-stream correlation joins related signals into root-cause events. Severity routing tags each event for the appropriate alert tier. False-positive suppression filters noise. Multi-stream subscription is the final stage that fans the curated alert stream out to the teams that need to see it. The six stages compose into one alert pipeline that produces signal at the consumer rather than noise at the broker.

What delivery adapters does the subscription layer support?

Slack channels per team and per location, email per role, PagerDuty for on-call rotations, webhook for downstream automation (the alert triggers a Zapier flow, a custom remediation script, or a dashboard update), Microsoft Teams for organizations on the Microsoft stack, and SMS for high-severity per-location notifications. Each adapter respects per-team rate limits, quiet hours, and channel preferences. The same alert can fan out to multiple adapters per consumer group when warranted.

Hire the agent that runs the anomaly pipeline

The anomaly-detection agent owns the six-axis pipeline — Detect, Forecast, Correlate, Route, Suppress, Subscribe — that turns raw multi-location signal into per-team curated alert streams across whichever event broker you license.

Hire the anomaly-detection agent

We scope on the call and send a private checkout link after.

Related reading: Two-sigma outlier flagging · Local-context change events